Effects of altered sensorimotor function of the tongue on spectral characteristics of sibilants /s, z/.
Bibliographic record
Abstract
Acoustic characteristics of sibilant sounds produced by 17 Canadian English-speaking female (6 patients) and male (11 patients) tongue cancer patients were studied. The patients had undergone a tongue resection of the anterior 2/3 of the tongue, and tongue reconstruction with a radial forearm free flap (RFFF). The acoustic data included measurements of the spectral moments (mean, standard deviation, skewness, and kurtosis) of sibilants /s, z/ analyzed from speech samples (stimulus sentences and Zoo Passage) which were recorded before the tongue resection, and 1 month, 6 months, and 12 months after the resection with RFFF reconstruction. Primarily, the tongue reconstruction with RFFF was found to result in temporary changes on the spectral characteristics such that speech output was found to approach the pre-operative speech quality over the 1-year period. In addition, RFFF reconstruction of the tongue was found to have some gender-specific effects in the acoustic characteristics analyzed. However, there was variability between patients, indicating individual compensatory and adaptive mechanisms for the altered sensorimotor function of the tongue following tongue reconstruction with RFFF. [The protocol for this study has been approved by the Health Research Ethics Board (HREB) of the University of Alberta (Edmonton, AB, Canada).]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".